Displaying similar documents to “Quasi-hierarchical evolution algorithm for flow assignment in survivable connection-oriented networks”

Ant algorithm for flow assignment in connection-oriented networks

Krzysztof Walkowiak (2005)

International Journal of Applied Mathematics and Computer Science

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This work introduces ANB (bf Ant Algorithm for bf Non-bf Bifurcated Flows), a novel approach to capacitated static optimization of flows in connection-oriented computer networks. The problem considered arises naturally from several optimization problems that have recently received significant attention. The proposed ANB is an ant algorithm motivated by recent works on the application of the ant algorithm to solving various problems related to computer networks. However, few works concern...

Anycasting in connection-oriented computer networks: Models, algorithms and results

Krzysztof Walkowiak (2010)

International Journal of Applied Mathematics and Computer Science

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Our discussion in this article centers around various issues related to the use of anycasting in connection-oriented computer networks. Anycast is defined as a one-to-one-of-many transmission to deliver a packet to one of many hosts. Anycasting can be applied if the same content is replicated over many locations in the network. Examples of network techniques that apply anycasting are Content Delivery Networks (CDNs), Domain Name Service (DNS), Peer-to-Peer (P2P) systems. The role of...

An Adaptation of the Hoshen-Kopelman Cluster Counting Algorithm for Honeycomb Networks

Popova, Hristina (2014)

Serdica Journal of Computing

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We develop a simplified implementation of the Hoshen-Kopelman cluster counting algorithm adapted for honeycomb networks. In our implementation of the algorithm we assume that all nodes in the network are occupied and links between nodes can be intact or broken. The algorithm counts how many clusters there are in the network and determines which nodes belong to each cluster. The network information is stored into two sets of data. The first one is related to the connectivity of the...

The branch and bound algorithm for a backup virtual path assignment in survivable ATM networks

Krzysztof Walkowiak (2002)

International Journal of Applied Mathematics and Computer Science

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Issues of network survivability are important, since users of computer networks should be provided with some guarantees of data delivery. A large amount of data may be lost in high-speed Asynchronous Transfer Mode (ATM) due to a network failure and cause significant economic loses. This paper addresses problems of network survivability. The characteristics of virtual paths and their influence on network restoration are examined. A new problem of Backup Virtual Path Routing is presented...

Localization in wireless sensor networks: Classification and evaluation of techniques

Ewa Niewiadomska-Szynkiewicz (2012)

International Journal of Applied Mathematics and Computer Science

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Recent advances in technology have enabled the development of low cost, low power and multi functional wireless sensing devices. These devices are networked through setting up a Wireless Sensor Network (WSN). Sensors that form a WSN are expected to be remotely deployed in large numbers and to self-organize to perform distributed sensing and acting tasks. WSNs are growing rapidly in both size and complexity, and it is becoming increasingly difficult to develop and investigate such large...

Variable Neighborhood Search for Solving the Capacitated Single Allocation Hub Location Problem

Maric, Miroslav (2013)

Serdica Journal of Computing

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In this paper a Variable Neighborhood Search (VNS) algorithm for solving the Capacitated Single Allocation Hub Location Problem (CSAHLP) is presented. CSAHLP consists of two subproblems; the first is choosing a set of hubs from all nodes in a network, while the other comprises finding the optimal allocation of non-hubs to hubs when a set of hubs is already known. The VNS algorithm was used for the first subproblem, while the CPLEX solver was used for the second. Computational results...

Learning Bayesian networks by Ant Colony Optimisation: searching in two different spaces.

Luis M. de Campos, José A. Gámez, José M. Puerta (2002)

Mathware and Soft Computing

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The most common way of automatically learning Bayesian networks from data is the combination of a scoring metric, the evaluation of the fitness of any given candidate network to the data base, and a search procedure to explore the search space. Usually, the search is carried out by greedy hill-climbing algorithms, although other techniques such as genetic algorithms, have also been used. A recent metaheuristic, Ant Colony Optimisation (ACO), has been successfully applied...